.png){kind=icon}
返回
Randomization Inference for Peer Effects
.png){kind=icon}
OA
AI
DOI：10.1080/01621459.2018.1512863.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Many previous causal inference studies require no interference, that is, the potential outcomes of a unit do not depend on the treatments of other units. However, this no-interference assumption becomes unreasonable when a unit interacts with other units in the same group or cluster. In a motivating application, a top Chinese university admits students through two channels: the college entrance exam (also known as Gaokao) and recommendation (often based on Olympiads in various subjects). The university randomly assigns students to dorms, each of which hosts four students. Students within the same dorm live together and have extensive interactions. Therefore, it is likely that peer effects exist and the no-interference assumption does not hold. It is important to understand peer effects, because they give useful guidance for future roommate assignment to improve the performance of students. We define peer effects using potential outcomes. We then propose a randomization-based inference framework to study peer effects with arbitrary numbers of peers and peer types. Our inferential procedure does not assume any parametric model on the outcome distribution. Our analysis gives useful practical guidance for policy makers of the university. for this article are available online.
Keyword:
Causal inference
Design-based inference
Grade point average (GPA)
Interference
Optimal treatment assignment
Spillover effect
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
J
IF:
3
论文数:
5.2K
被引数:
4.8W
机构
引用论文
Deterring or Displacing Electoral Irregularities? Spillover Effects of Observers in a Randomized Field Experiment in Ghana.png)
JOURNAL OF POLITICSOAAI
JOURNAL OF POLITICSIF3.8
OAAI分享收藏Correlation-Based Pattern Recognition and Its Application to Adaptive Soft-Sensor Design基于相关性的模式识别及其在自适应软传感器设计中的应用
PREAI
分享收藏
查看更多内容